ADDED MLX FOR SUPER FAST GENERATIONS ON APPLE SILICON

- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Updated release workflow to include MLX-specific dependencies and configurations for macOS platforms.
- Refactored backend code to dynamically select between MLX and PyTorch based on the runtime environment.
- Enhanced model loading and inference logic to accommodate backend-specific requirements, including updated model IDs and hidden imports.
- Improved health check and model status reporting to reflect the active backend type.
- Streamlined caching mechanisms to support both backend types, ensuring compatibility and performance.

backend/models.py

@@ -126,6 +126,7 @@ class HealthResponse(BaseModel):
     gpu_available: bool
     gpu_type: Optional[str] = None  # GPU type (CUDA, MPS, or None)
     vram_used_mb: Optional[float] = None
+    backend_type: Optional[str] = None  # Backend type (mlx or pytorch)
 
 
 class ModelStatus(BaseModel):